Assessing the burden of rare DNA methylation deviations in schizophrenia

Abstract

AbstractAlong with case-control group differences in DNA methylation (DNAm) identified in epigenomewide association studies (EWAS), multiple rare DNAm outliers may exist in subsets of cases, underlying the etiological heterogeneity of some disorders. This creates an impetus for novel approaches focused on detecting rare/private outliers in the individual methylomes. Here, we present a novel, data-driven method - Outlier Methylation Analysis (OMA) – which through optimization detects genomic regions with strongly deviating DNAm levels, which we call outlier methylation regions (OMRs).Focusing on schizophrenia (SCZ) - a neuropsychiatric disorder with a heterogeneous etiology – we applied the OMA method in two independent, publicly available SCZ case-control samples with DNAm array information. We found SCZ cases had an increased burden of OMRs compared to controls (IRR=1.22, p=1.8×10-8), and case OMRs were enriched in regions relevant to cellular differentiation and development (i.e. polycomb repressed elements in the Gm12878 differentiated cell line, p=1.9×10-5, and poised promoters in the H1hesc stem cell line, p=5.4×10-4). Furthermore, SCZ cases were ~2.5-fold enriched (p=1.1×10-3) for OMRs overlapping genesets associated with developmental processes. The OMR burden was reduced in clozapine-treated, compared to untreated, SCZ cases (IRR=0.88, p=9.5×10-3), and also associated with increased chronological age (IRR=1.01, p= 2.7×10-16).Our findings demonstrate an elevated burden of OMRs in SCZ, implying methylomic dysregulation in SCZ which could correspond to the etiological heterogeneity among cases. These results remain to be causally examined and replicated in other cohorts and tissues. For this, and applications in other traits, we offer the OMA method to the scientific community.